Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

“Core” PCE inflation—that is, inflation in PCE-ex-food-and-energy—is widely used as an estimate of trend inflation. But it is long overdue for replacement. The original rationale of core inflation was to remove volatile items with transitory shocks. But aside from gasoline, the list of excluded items is far from optimal. Core inflation also su˙ers from other severe deficiencies, common to all exclusion indexes. Excluded items often have persistent trends; thus excluding them imparts a significant time-varying bias. Items that are not excluded can experience high volatility, and will cause exclusion indexes to depart notably from trend inflation—as core PCE has done at crucial moments. Two other prominent trend inflation measures, trimmed mean PCE inflation and median PCE inflation, gracefully address these issues (though neither is perfect). A wide variety of evidence comparing these three trend measures is provided. The findings indicate that, for a variety of considerations that are relevant for both trend inflation estimation and for monetary policy deliberations and communication, either trimmed mean PCE inflation or median PCE inflation are superior measures.

This paper finds strong support for a Phillips curve that becomes nonlinear when inflation is “low”—which our baseline model defines as less than 3 percent. The nonlinear curve is steep when output is above potential (slack is negative) but flat when output is below potential (slack is positive) so that further increases in economic slack have little effect on inflation. This finding is consistent with evidence of downward nominal wage and price rigidity. When inflation is high, the Phillips curve is linear and relatively steep. These results are robust to placing the threshold between the high and low inflation regimes at 2, 3, or 4 percent inflation or for a threshold based on country-specific medians of inflation. In this nonlinear model, international factors play a large role in explaining headline inflation (albeit less so for core inflation), a role that has been increasing since the global financial crisis. These results provide evidence of channels which could boost inflation in the future, even if they were dormant before the Covid pandemic.

Using 100+ years of data from 18 developed countries, we use a frequency domain technique to compute “long-run” correlations between inflation on the one hand and money growth and nominal interest rates on the other. The estimated long-run correlations are almost always positive. Their magnitude is relatively substantial for money growth, more modest for interest rates. We conclude that some traditional propositions about monetary neutrality are broadly consistent with the data.

This article aims to evaluate quantitative inflation forecasts for the Chilean economy, taking advantage of a specific survey of consumer perceptions at the individual microdata level, which, at the same time, is linked to a survey of employment in Chile’s capital city. Thus, it is possible to link, with no error, consumer perceptions and 12-month-ahead inflation forecasts with personal characteristics such as gender, age, educational level, county of living, and the economic sector in which they are currently working. By using a sample ranging from 2005.III to 2018.IV, the results suggest that women aged between 35 and 65 years old, with a college degree, living in the North-eastern part of Santiago (the richest of the city), and working in the Community and Social Services sector are the best forecasters. Men aged between 35 and 65 years old, with a college degree, in a tie living in the North-eastern and South-eastern zones but working in Government and Financial Services and Retail sectors, respectively, come in second. Some econometric exercises reinforce and give greater support to the group of most accurate forecasters and reveal that another group of forecasters, different from the second-best in terms of forecast accuracy, displays the characteristics required of a forecasting variable. Remarkably, this group has the same specifications as the most accurate group, with the only difference being that it is composed of men instead of women. Thus, it looks promising for further consideration. Importantly, a forecast accuracy test reveals that no factor comes out as superior to the naïve random walk forecast used as a benchmark. These results are important because they help to identify the most accurate group when forecasting inflation and, thus, help refine the information provided by the survey for inflation forecasting purposes.

One major outcome of the Federal Reserve’s 2019–20 framework review was the adoption of a Flexible Average Inflation Targeting (FAIT) strategy in August 2020. Using synthetic control methods, we document that U.S. inflation rose post-FAIT considerably more than predicted had the strategy not changed (an average of 1.18 percentage points during 2020:M8–2022:M2). To explore the extent to which targeting average inflation delayed the Fed’s response and contributed to post-FAIT inflation, we adopt a version of the open-economy New Keynesian model in Martínez-García (2021) and document the economic consequences of adopting alternative measures of average inflation as policy objectives. We document three additional major findings using this general equilibrium setup: First, depending on how far back and how much weight is assigned to past inflation misses, the policy outcomes under FAIT are similar to those under the pre-FAIT regime. Secondly, we find that the implementation of FAIT can have large effects over short periods of time as it tends to delay action. However, over longer periods of time—such as the 1984:Q1–2019:Q4 pre-FAIT period—its effects wash out and appear negligible. Finally, we find that di˙erent average inflation measures explain an average of 0.5 percentage points per quarter of the post-FAIT inflation surge, indicating that targeting average inflation by itself can only explain part of the inflation spike since August 2020.

We investigate the factors underlying poverty transitions in Nairobi’s slums focusing on whether differences in characteristics make people more prone to enter poverty and persist in, or whether past experience of poverty matters on future states. Understanding these issues is essential for the design of effective policy programs aimed at enhancing the lives of the poor. The paper uses an endogenous switching model, which accounts for initial conditions, non-random attrition, and unobserved heterogeneity.Estimations are based on a panel dataset from the Nairobi Demographic Surveillance System. Results indicate that true state dependence (TSD) constitutes the major factor driving poverty persistence. There are little heterogeneity effects. Even when household and individual observed characteristics differ notably, the TSD size remains very large. Active anti-poverty programs aimed at breaking the cycle of poverty constitute then the most appropriate policies for taking people out of poverty and preventing them to fall back in.

Abstract We present the results of a questionnaire study in Belgium, Burkina Faso and Indonesia focusing on the problem of the just allocation of an indivisible good. The formal axioms proposed in social choice theory offer an attractive framework to structure the response patterns. Interindividual differences can be interpreted in a meaningful way in terms of basic intuitions about desert, efficiency and compensation. Belgian students are most resource-egalitarian, Burkinese students attach a large weight to innate capacities, Indonesian students focus on actual production. The crucial no-envy criterion is supported by a majority of respondents, but this majority becomes small if there is an unavoidable conflict between no-envy and the “responsibility” requirement of the stand-alone upper bound. We discuss the pros and cons of questionnaire-experimental studies as compared to large representative surveys.

In this paper we introduce a “power booster factor” for out-of-sample tests of predictability. The relevant econometric environment is one in which the econometrician wants to compare the population Mean Squared Prediction Errors (MSPE) of two models: one big nesting model, and another smaller nested model. Although our factor can be used to improve finite sample properties of several out-of-sample tests of predictability, in this paper we focus on the widely used test developed by Clark and West (2006, 2007). Our new test multiplies the Clark and West t-statistic by a factor that should be close to one under the null hypothesis that the short nested model is the true model, but that should be greater than one under the alternative hypothesis that the big nesting model is more adequate. We use Monte Carlo simulations to explore the size and power of our approach. Our simulations reveal that the new test is well sized and powerful. In particular, it tends to be less undersized and more powerful than the test by Clark and West (2006, 2007). Although most of the gains in power are associated to size improvements, we also obtain gains in size-adjusted-power. Finally we illustrate the use of our approach when evaluating the ability that an international core inflation factor has to predict core inflation in a sample of 30 OECD economies. With our “power booster factor” more rejections of the null hypothesis are obtained, indicating a strong influence of global inflation in a selected group of these OECD countries.

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